Insights into the long-term pollution trends and sources contributions in Lake Taihu, China using multi-statistic analyses models. (March 2020)

• Record Type: Journal Article
• Authors: Liu, Lili; Dong, Yongcheng; Kong, Ming; Zhou, Jian; Zhao, Hanbin; Tang, Zhou; Zhang, Meng; Wang, Zhiping
• Is Part Of: Chemosphere. Volume 242(2020)
• Publication Date: 2020-03
• Subjects: Water quality -- Source apportionment -- Positive matrix factorization model -- Absolute principal component score-multiple linear regression model -- Lake Taihu; Pollution -- Periodicals; Pollution -- Physiological effect -- Periodicals; Environmental sciences -- Periodicals; Atmospheric chemistry -- Periodicals; 551.511
• DOI: 10.1016/j.chemosphere.2019.125272
• Languages: English
• ISSNs: 0045-6535

Abstract

Abstract: Eutrophication pollution seriously threatens the sustainable development of Lake Taihu, China. In order to identify the primary parameters of water quality and the potential pollution sources, the water quality dataset of Lake Taihu (2010–2014) was analyzed with the water quality index (WQI) and multivariate statistical analysis methods. Principle component analysis/factor analysis (PCA/FA) and correlation analysis screened out five significant water quality indicators, i.e. potassium permanganate index (CODMn ), total nitrogen (TN), total phosphorus (TP), chloride ion (Cl − ) and dissolved oxygen (DO), to represent the whole datasets and evaluate the water quality with WQI. Since northwestern of Lake Taihu was the most heavily polluted area, the parameters of the water quality were analyzed to further explore the potential sources and their contributions. Five potential pollution sources of northwestern lake were identified, and the contribution rate of each pollution source was calculated by the absolute principal component score-multiple linear regression (APCS-MLR) and positive matrix factorization (PMF) models. In brief, the PMF model was more suitable for pollution source apportionment of the northwestern lake, and the contribution rate was ranked as agricultural non-point source pollution (26.6%) > domestic sewage discharge (23.5%) > industrial wastewater discharge and atmospheric deposition (20.6%) > phytoplankton growth (16.0%) > rainfall or wind disturbance (13.4%). This study might provide useful information for the optimization of water quality management and pollution control strategies of Lake Taihu. Graphical abstract: Image 1 Highlights: PCA/FA and correlation analysis screened out primary water quality parameters. WQI and CA identified spatiotemporal distribution characteristics of water quality. PCA/FA identified potential pollution sources in the heavily polluted area. The APCS-MLR and PMF figured out contributions of pollution sources in distinct areas.